**Acknowledgements**

*Modern Applications of Electrostatics and Dielectrics*

using electrovibration attraction.

perception.

**4. Conclusions**

[45–51], little work has been done on variable friction displays and particularly

An electrostatic friction display creates clearly perceptible stimuli when the surface is laterally scanned, but not when the finger is stationary. This fundamental limitation has confined the application of electrostatic friction displays mostly to texture rendering. In the only relevant work [18], Ilkhani et al. proposed a data-driven texture rendering method by recording accelerations from three real materials and playing them back on an electrovibration display. Their automated data collection is done under single constraint condition (contact force 0.35 N and scanning velocity 0.74 m/s) using a servomotor controlled by an Arduino Uno. They conducted a user study to compare the perceived surface roughness generated with their data-driven signals and with that of square wave signals. The frequency of each square wave is set based on the main frequency of the corresponding acceleration. Using a visual indicator, they made the user to keep a constant scanning velocity, but not equal to the data collection velocity and presumably very slower than that. In addition, there is no mention of contact force status during experimentation. Nevertheless, they reported higher percentage of similarity between data-driven textures and real ones than square wave patterns. In their extended work [52], they applied the same approach on the data from Penn Haptic Texture Toolkit [53] and performed MDS analysis to create a perceptual space and to extract underlying dimensions of the textures. Their results showed roughness and stickiness as the primary dimensions of texture

In ref. [54], a data-driven neural network for realistic texture rendering on an electrovibration display is proposed. First, a motorized linear tribometer is developed to collect lateral frictional forces from the textured surfaces under various scanning velocities and normal forces. Then an inverse dynamics model of the display is created to describe its output-input relationship using nonlinear autoregressive with external input (NARX) neural networks. Forces resulting from applying a full-band pseudorandom binary signal (PRBS) to the display are used to train each network under the given experimental condition. A comparison between the real and virtual forces in frequency domain shows promising results and reveals

In this chapter, we have introduced the concept behind electrostatic friction displays (also called electrovibration displays) and their potential applications for shape and texture rendering. The potential uses for the technique are exciting. Electrovibration could make interactive textbooks more engaging on tablets, allowing students to explore the three-dimensional features of an object directly on each page. Software for iOS or Android could be augmented with unique haptic feedback for button presses and swipe gestures. Games could incorporate electrovibration to add a new layer of interactivity to touch controls. With some smart design, it could really improve the functionality of touchscreens used in other fields, as well. For instance, the use of touchscreens in automobiles to navigate the map or control the music playback persuades drivers to avert their eyes from the road. Possibly, with an appropriate design, the same control functionalities could be delivered using a variable touch-based feedback without the need to take our eyes off the road. Given the commonness of capacitive touchscreens, the addition of richer tactile feedback through electrovibration promises to enhance almost all of our interactions with

the capabilities and limitations of the proposed technique.

**48**

digital contents.

I would like to thank Prof. Seungmoon Choi for his extensive personal and professional guidance teaching me a great deal about both scientific research and life in general. As my teacher and mentor, he has taught me more than I could ever give him credit for here. He has shown me, by his example, what a good scientist (and person) should be. I also would like to thank Dr. Jin Ryung Kim for his constant support and encouragement.
